Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 62092

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-10T22:37:38+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

その他のオープンデータ。

• 新型コロナウイルス対策病床オープンデータ

　

Data Wrangling

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	112169
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	110456	0
dateAnnounced	0	1.00	10	10	0	287	0
gender	18958	0.83	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	108160	0.04	8	23	0	8	0
notes	58357	0.48	1	270	0	51040	1
mhlwPatientNumber	111720	0.00	1	11	0	434	0
prefecturePatientNumber	16559	0.85	5	20	0	95601	0
prefectureSourceURL	80858	0.28	5	224	0	3441	0
residence	26850	0.76	1	38	0	1423	0
sourceURL	637	0.99	1	239	0	8268	0
relatedPatients	101547	0.09	2	259	0	6444	0
knownCluster	109686	0.02	3	88	0	229	0
detectedCityTown	85867	0.23	2	22	0	663	0
cityPrefectureNumber	86128	0.23	1	34	0	26032	2
citySourceURL	100294	0.11	9	317	0	3649	0
deceasedDate	110319	0.02	10	10	0	237	0
deceasedReportedDate	110952	0.01	10	62	0	206	0
deathSourceURL	111097	0.01	14	123	0	653	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.98	TRU: 110455, FAL: 1714
charterFlightPassenger	112155	0	1.00	TRU: 14
cruisePassengerDisembarked	112158	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	31.38	23.97	-1	20	30	50	100	▅▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`) %>% 
  dplyr::rename(Pref = `都道府県`, region = `八地方区分`)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	110455
Number of columns	19
_______________________
Column type frequency:
character	3
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	110455	0
pref	0	1.00	3	15	0	49	0
knownCluster	108001	0.02	3	88	0	227	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-10	2020-08-16	287
deceasedDate	110076	0	2020-02-13	2020-10-17	2020-05-08	150
deceasedReportedDate	110125	0	2020-02-13	2020-10-17	2020-05-16	131

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	18268	0.83	FALSE	2	M: 51754, F: 40433
patientStatus	107922	0.02	FALSE	8	Hos: 1261, Dec: 371, Hom: 315, Dis: 283
ageBracket	0	1.00	FALSE	13	20: 25243, -1: 18365, 30: 15999, 40: 13292
pcode	1291	0.99	FALSE	47	13: 33098, 27: 14120, 14: 9489, 23: 7005
Pref	1291	0.99	FALSE	47	東京都: 33098, 大阪府: 14120, 神奈川: 9489, 愛知県: 7005
region	1291	0.99	FALSE	8	関東地: 56657, 近畿地: 22173, 九州地: 11322, 中部地: 10881
広域圏	9231	0.92	FALSE	8	首都圏: 56905, 近畿圏: 21575, 中部圏: 9516, 九州圏: 7758
通俗的区分	1291	0.99	FALSE	11	関東: 56657, 関西: 21575, 東海: 9130, 九州: 7758
fct_pref	1291	0.99	FALSE	47	Tok: 33098, Osa: 14120, Kan: 9489, Aic: 7005

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 110455
charterFlightPassenger	110441	0	1.00	TRU: 14
cruisePassengerDisembarked	110444	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 108001, TRU: 2454

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合はサマリデータを使った方がいい。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

集計（陽性者）

　

地方別集計

地方別で陽性者数と人口千人あたりの比率を比較する。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_region

　

都道府県別集計

同様に都道府県別に比較する。

r_by_prefs <- x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_prefs %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

累計陽性者数トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = n, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

人口千人あたりの比率トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = rate, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

クラスタ比率

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1))

　

x %>% 
  dplyr::group_by(Pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

日次集計（陽性者）

　

全国日次集計

日次単位の陽性者数、前日差、累計、移動平均（7日）を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n),
                ma7 = zoo::rollmeanr(n, k = 7L, na.pad = TRUE))

x_by_all

　

地方区分別日次集計

同様に地方区分別の集計を求める。

x_by_region <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::select(date, region, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_region

　

都道府県別日次集計

同様に都道府県別の集計を求める。

x_by_prefs <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "Pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_prefs

　

日次集計（死亡者）

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, Pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(Pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "Pref", values_to = "n") %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = Pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_prefs, ., by = "Pref") %>% 
  dplyr::select(Pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

　

陽性者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の陽性者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の陽性者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

陽性者数（単日）

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("地方別陽性者数推移（単日） @", datetime),
                  caption = caption, 
                  x = "", y = "陽性者数") 

　

移動平均

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("地方別7日間移動平均 @", datetime),
                  caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 3) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

累積

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line() +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("地方別累積陽性者数 @", datetime),
                  caption = caption,
                  x = "", y = "累積陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)))

　

単日＋累積

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_prefs %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 195.2940 202.9186 215.6051 240.9876 239.8843 260.9643 257.6981 270.5000
##  [9] 284.4354 292.8605 313.5489 316.9759 332.3407 336.6218
## 
## $北海道地方$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 185.6842 180.5971
## 44.14286 192.4062 186.8413
## 44.28571 202.1898 195.0882
## 44.42857 226.0921 218.2069
## 44.57143 222.8081 213.7686
## 44.71429 242.1609 232.2070
## 44.85714 236.9045 225.8970
## 45.00000 246.4642 233.7405
## 45.14286 258.0079 244.0181
## 45.28571 263.5866 248.0899
## 45.42857 281.7131 264.8602
## 45.57143 282.3981 264.0938
## 45.71429 295.1079 275.3981
## 45.85714 296.6498 275.4899
## 
## $北海道地方$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 204.9038 209.9909
## 44.14286 213.4309 218.9958
## 44.28571 229.0204 236.1220
## 44.42857 255.8831 263.7683
## 44.57143 256.9604 265.9999
## 44.71429 279.7677 289.7216
## 44.85714 278.4917 289.4991
## 45.00000 294.5357 307.2595
## 45.14286 310.8628 324.8526
## 45.28571 322.1344 337.6310
## 45.42857 345.3848 362.2377
## 45.57143 351.5536 369.8579
## 45.71429 369.5735 389.2833
## 45.85714 376.5938 397.7537
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 22.62082 25.11846 23.08291 21.74902 22.51790 19.69778 23.07173 22.58027
##  [9] 21.58037 23.07844 22.95867 23.24255 24.07310 22.48325
## 
## $東北地方$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 16.04269 12.560434
## 44.14286 18.35852 14.780016
## 44.28571 16.14591 12.473690
## 44.42857 14.63939 10.875770
## 44.57143 15.23972 11.386879
## 44.71429 12.25486  8.314818
## 44.85714 15.46765 11.442296
## 45.00000 14.62355 10.411524
## 45.14286 13.43495  9.123024
## 45.28571 14.74859 10.339035
## 45.42857 14.44839  9.943316
## 45.57143 14.55558  9.956971
## 45.71429 15.21296 10.522691
## 45.85714 13.45327  8.673089
## 
## $東北地方$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 29.19896 32.68121
## 44.14286 31.87841 35.45691
## 44.28571 30.01991 33.69213
## 44.42857 28.85866 32.62227
## 44.57143 29.79609 33.64893
## 44.71429 27.14069 31.08074
## 44.85714 30.67581 34.70117
## 45.00000 30.53698 34.74901
## 45.14286 29.72578 34.03771
## 45.28571 31.40829 35.81784
## 45.42857 31.46895 35.97403
## 45.57143 31.92952 36.52812
## 45.71429 32.93324 37.62351
## 45.85714 31.51323 36.29342
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 475.8703 574.5616 562.6583 579.5243 418.7507 324.7723 500.2696 473.6261
##  [9] 582.3032 574.6799 593.2264 432.9537 338.9456 514.1790
## 
## $関東地方$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 411.5565 377.5108
## 44.14286 496.1128 454.5846
## 44.28571 476.5308 430.9376
## 44.42857 488.0637 439.6474
## 44.57143 323.0653 272.4125
## 44.71429 225.4994 172.9476
## 44.85714 397.8387 343.6151
## 45.00000 361.6379 302.3550
## 45.14286 464.0209 401.4060
## 45.28571 451.5489 386.3673
## 45.42857 466.0298 398.6960
## 45.57143 302.1945 232.9748
## 45.71429 204.9925 134.0820
## 45.85714 377.3278 304.8832
## 
## $関東地方$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 540.1841 574.2298
## 44.14286 653.0104 694.5387
## 44.28571 648.7859 694.3791
## 44.42857 670.9850 719.4013
## 44.57143 514.4362 565.0890
## 44.71429 424.0452 476.5971
## 44.85714 602.7005 656.9242
## 45.00000 585.6142 644.8972
## 45.14286 700.5856 763.2004
## 45.28571 697.8110 762.9926
## 45.42857 720.4230 787.7568
## 45.57143 563.7129 632.9326
## 45.71429 472.8986 543.8091
## 45.85714 651.0302 723.4748
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 143.6052 165.9171 175.9047 192.4146 170.0318 153.8676 186.5764 181.3376
##  [9] 179.0882 201.2866 213.4217 190.1516 193.1747 209.6305
## 
## $中部地方$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 125.3444 115.67772
## 44.14286 142.3698 129.90459
## 44.28571 147.3473 132.22996
## 44.42857 158.9957 141.30475
## 44.57143 133.6241 114.35107
## 44.71429 113.4356  92.03216
## 44.85714 143.2154 120.26150
## 45.00000 132.0266 105.92289
## 45.14286 124.3168  95.32266
## 45.28571 142.3345 111.12723
## 45.42857 149.6065 115.82477
## 45.57143 122.4993  86.68629
## 45.71429 121.7375  83.92093
## 45.85714 134.2602  94.36151
## 
## $中部地方$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 161.8659 171.5326
## 44.14286 189.4644 201.9296
## 44.28571 204.4621 219.5795
## 44.42857 225.8335 243.5244
## 44.57143 206.4395 225.7125
## 44.71429 194.2996 215.7030
## 44.85714 229.9374 252.8913
## 45.00000 230.6485 256.7522
## 45.14286 233.8595 262.8537
## 45.28571 260.2386 291.4459
## 45.42857 277.2368 311.0185
## 45.57143 257.8040 293.6169
## 45.71429 264.6118 302.4284
## 45.85714 285.0009 324.8995
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 235.7536 270.1154 311.8908 311.4419 254.1070 188.9882 335.8921 266.1950
##  [9] 300.5243 340.3954 339.6114 280.7484 215.1214 360.7603
## 
## $近畿地方$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 204.0251 187.2291
## 44.14286 233.3877 213.9453
## 44.28571 272.8043 252.1131
## 44.42857 269.0190 246.5616
## 44.57143 209.7275 186.2344
## 44.71429 142.1801 117.4014
## 44.85714 287.4421 261.7942
## 45.00000 208.5634 178.0551
## 45.14286 238.7725 206.0831
## 45.28571 275.5280 241.1892
## 45.42857 271.6135 235.6177
## 45.57143 210.2292 172.8986
## 45.71429 142.1335 103.4961
## 45.85714 285.6981 245.9626
## 
## $近畿地方$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 267.4821 284.2781
## 44.14286 306.8430 326.2854
## 44.28571 350.9774 371.6685
## 44.42857 353.8649 376.3222
## 44.57143 298.4866 321.9797
## 44.71429 235.7963 260.5750
## 44.85714 384.3422 409.9901
## 45.00000 323.8266 354.3350
## 45.14286 362.2762 394.9656
## 45.28571 405.2628 439.6016
## 45.42857 407.6092 443.6050
## 45.57143 351.2676 388.5981
## 45.71429 288.1093 326.7468
## 45.85714 435.8225 475.5580
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 11.18346 11.33855 11.33855 11.33855 11.33855 11.33855 11.33855 11.33855
##  [9] 11.33855 11.33855 11.33855 11.33855 11.33855 11.33855
## 
## $中国地方$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                  80%       95%
## 44.00000  2.18629578 -2.576517
## 44.14286  1.93685977 -3.040096
## 44.28571  1.75090787 -3.324484
## 44.42857  1.56849452 -3.603462
## 44.57143  1.38942508 -3.877325
## 44.71429  1.21352214 -4.146345
## 44.85714  1.04062343 -4.410771
## 45.00000  0.87058008 -4.670830
## 45.14286  0.70325513 -4.926731
## 45.28571  0.53852225 -5.178668
## 45.42857  0.37626456 -5.426820
## 45.57143  0.21637374 -5.671352
## 45.71429  0.05874915 -5.912418
## 45.85714 -0.09670295 -6.150161
## 
## $中国地方$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 20.18063 24.94344
## 44.14286 20.74024 25.71720
## 44.28571 20.92620 26.00159
## 44.42857 21.10861 26.28057
## 44.57143 21.28768 26.55443
## 44.71429 21.46358 26.82345
## 44.85714 21.63648 27.08788
## 45.00000 21.80652 27.34793
## 45.14286 21.97385 27.60384
## 45.28571 22.13858 27.85577
## 45.42857 22.30084 28.10392
## 45.57143 22.46073 28.34846
## 45.71429 22.61836 28.58952
## 45.85714 22.77381 28.82727
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 1.947222 1.612291 1.612291 1.612291 1.612291 1.612291 1.612291 1.612291
##  [9] 1.612291 1.612291 1.612291 1.612291 1.612291 1.612291
## 
## $四国地方$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -1.527934 -3.367570
## 44.14286 -2.117041 -4.091230
## 44.28571 -2.188265 -4.200157
## 44.42857 -2.258178 -4.307081
## 44.57143 -2.326851 -4.412107
## 44.71429 -2.394347 -4.515333
## 44.85714 -2.460724 -4.616848
## 45.00000 -2.526037 -4.716736
## 45.14286 -2.590336 -4.815071
## 45.28571 -2.653665 -4.911925
## 45.42857 -2.716067 -5.007361
## 45.57143 -2.777583 -5.101442
## 45.71429 -2.838248 -5.194221
## 45.85714 -2.898098 -5.285753
## 
## $四国地方$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 5.422378 7.262015
## 44.14286 5.341622 7.315811
## 44.28571 5.412846 7.424739
## 44.42857 5.482760 7.531662
## 44.57143 5.551432 7.636688
## 44.71429 5.618928 7.739914
## 44.85714 5.685306 7.841430
## 45.00000 5.750619 7.941318
## 45.14286 5.814917 8.039653
## 45.28571 5.878246 8.136506
## 45.42857 5.940649 8.231943
## 45.57143 6.002164 8.326023
## 45.71429 6.062830 8.418803
## 45.85714 6.122679 8.510335
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 56.54673 62.39840 59.20258 53.97751 56.57599 48.75371 58.54661 57.25835
##  [9] 61.64408 60.84277 57.12110 58.89015 55.55265 59.33867
## 
## $九州地方$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%        95%
## 44.00000  34.594077  22.973042
## 44.14286  35.831847  21.768362
## 44.28571  29.456787  13.710312
## 44.42857  23.286925   7.040311
## 44.57143  23.837520   6.506821
## 44.71429  11.835296  -7.708134
## 44.85714  17.875595  -3.654342
## 45.00000  10.377539 -14.439661
## 45.14286  10.140781 -17.123420
## 45.28571   5.269803 -24.148747
## 45.42857  -1.680337 -32.807937
## 45.57143  -3.320043 -36.252130
## 45.71429 -10.407779 -45.325119
## 45.85714 -10.169942 -46.965580
## 
## $九州地方$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000  78.49939  90.12042
## 44.14286  88.96496 103.02844
## 44.28571  88.94838 104.69486
## 44.42857  84.66809 100.91470
## 44.57143  89.31446 106.64516
## 44.71429  85.67213 105.21556
## 44.85714  99.21763 120.74757
## 45.00000 104.13916 128.95636
## 45.14286 113.14738 140.41158
## 45.28571 116.41573 145.83428
## 45.42857 115.92254 147.05014
## 45.57143 121.10034 154.03243
## 45.71429 121.51307 156.43041
## 45.85714 128.84729 165.64292

　

都道府県別

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 195.2940 202.9186 215.6051 240.9876 239.8843 260.9643 257.6981 270.5000
##  [9] 284.4354 292.8605 313.5489 316.9759 332.3407 336.6218
## 
## $北海道$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 185.6842 180.5971
## 44.14286 192.4062 186.8413
## 44.28571 202.1898 195.0882
## 44.42857 226.0921 218.2069
## 44.57143 222.8081 213.7686
## 44.71429 242.1609 232.2070
## 44.85714 236.9045 225.8970
## 45.00000 246.4642 233.7405
## 45.14286 258.0079 244.0181
## 45.28571 263.5866 248.0899
## 45.42857 281.7131 264.8602
## 45.57143 282.3981 264.0938
## 45.71429 295.1079 275.3981
## 45.85714 296.6498 275.4899
## 
## $北海道$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 204.9038 209.9909
## 44.14286 213.4309 218.9958
## 44.28571 229.0204 236.1220
## 44.42857 255.8831 263.7683
## 44.57143 256.9604 265.9999
## 44.71429 279.7677 289.7216
## 44.85714 278.4917 289.4991
## 45.00000 294.5357 307.2595
## 45.14286 310.8628 324.8526
## 45.28571 322.1344 337.6310
## 45.42857 345.3848 362.2377
## 45.57143 351.5536 369.8579
## 45.71429 369.5735 389.2833
## 45.85714 376.5938 397.7537
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 3.118871 1.667455 2.587582 2.004266 2.374060 2.139629 2.288247 2.194030
##  [9] 2.253759 2.215894 2.239899 2.224681 2.234328 2.228212
## 
## $青森県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%       95%
## 44.00000  0.5688686 -0.781021
## 44.14286 -1.3236502 -2.907046
## 44.28571 -0.4738284 -2.094441
## 44.42857 -1.2949579 -3.041462
## 44.57143 -1.0363549 -2.841720
## 44.71429 -1.4425289 -3.338809
## 44.85714 -1.4174118 -3.379069
## 45.00000 -1.6553425 -3.693077
## 45.14286 -1.7191094 -3.822219
## 45.28571 -1.8859108 -4.057275
## 45.42857 -1.9812364 -4.215770
## 45.57143 -2.1159895 -4.413802
## 45.71429 -2.2205143 -4.578766
## 45.85714 -2.3392814 -4.757166
## 
## $青森県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 5.668873 7.018763
## 44.14286 4.658561 6.241956
## 44.28571 5.648993 7.269606
## 44.42857 5.303491 7.049995
## 44.57143 5.784475 7.589840
## 44.71429 5.721787 7.618067
## 44.85714 5.993906 7.955563
## 45.00000 6.043403 8.081138
## 45.14286 6.226628 8.329737
## 45.28571 6.317699 8.489063
## 45.42857 6.461034 8.695568
## 45.57143 6.565351 8.863163
## 45.71429 6.689171 9.047422
## 45.85714 6.795706 9.213591
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 0.2584580 0.2478335 0.6127907 0.2573598 0.2584572 0.5796626 0.6979516
##  [8] 0.3414759 0.3414759 0.3414759 0.3414759 0.3414759 0.3414759 0.3414759
## 
## $岩手県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                  80%        95%
## 44.00000 -0.33685181 -0.6519898
## 44.14286 -0.34804059 -0.6634773
## 44.28571  0.01635283 -0.2993823
## 44.42857 -0.33964134 -0.6556747
## 44.57143 -0.33910666 -0.6554379
## 44.71429 -0.01846340 -0.3350922
## 44.85714  0.09926387 -0.2176623
## 45.00000 -0.29537424 -0.6325024
## 45.14286 -0.29629482 -0.6339103
## 45.28571 -0.29721407 -0.6353161
## 45.42857 -0.29813201 -0.6367200
## 45.57143 -0.29904862 -0.6381218
## 45.71429 -0.29996393 -0.6395217
## 45.85714 -0.30087793 -0.6409195
## 
## $岩手県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%      95%
## 44.00000 0.8537678 1.168906
## 44.14286 0.8437076 1.159144
## 44.28571 1.2092286 1.524964
## 44.42857 0.8543609 1.170394
## 44.57143 0.8560211 1.172352
## 44.71429 1.1777887 1.494418
## 44.85714 1.2966393 1.613565
## 45.00000 0.9783261 1.315454
## 45.14286 0.9792467 1.316862
## 45.28571 0.9801659 1.318268
## 45.42857 0.9810839 1.319672
## 45.57143 0.9820005 1.321074
## 45.71429 0.9829158 1.322474
## 45.85714 0.9838298 1.323871
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 13.08989 16.10615 13.78689 15.57021 14.19898 15.25335 14.44263 15.06601
##  [9] 14.58668 14.95524 14.67185 14.88975 14.72220 14.85104
## 
## $宮城県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%      95%
## 44.00000  8.573704 6.182979
## 44.14286 11.546564 9.132865
## 44.28571  8.828216 6.203250
## 44.42857 10.524534 7.853513
## 44.57143  8.870061 6.049100
## 44.71429  9.809334 6.927448
## 44.85714  8.775251 5.775122
## 45.00000  9.268754 6.199874
## 45.14286  8.600182 5.431122
## 45.28571  8.832734 5.591674
## 45.42857  8.381175 5.051095
## 45.57143  8.461951 5.059277
## 45.71429  8.140254 4.655981
## 45.85714  8.133529 4.577496
## 
## $宮城県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 17.60608 19.99680
## 44.14286 20.66574 23.07943
## 44.28571 18.74557 21.37053
## 44.42857 20.61589 23.28691
## 44.57143 19.52791 22.34887
## 44.71429 20.69736 23.57925
## 44.85714 20.11001 23.11013
## 45.00000 20.86326 23.93214
## 45.14286 20.57318 23.74224
## 45.28571 21.07775 24.31881
## 45.42857 20.96252 24.29260
## 45.57143 21.31756 24.72023
## 45.71429 21.30415 24.78842
## 45.85714 21.56854 25.12458
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 0.3357190 0.2261832 0.2261832 0.2261832 0.2261832 0.2261832 0.2261832
##  [8] 0.2261832 0.2261832 0.2261832 0.2261832 0.2261832 0.2261832 0.2261832
## 
## $秋田県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%       95%
## 44.00000 -0.8931485 -1.543672
## 44.14286 -1.0141244 -1.670703
## 44.28571 -1.0141244 -1.670703
## 44.42857 -1.0141244 -1.670703
## 44.57143 -1.0141244 -1.670703
## 44.71429 -1.0141244 -1.670703
## 44.85714 -1.0141244 -1.670703
## 45.00000 -1.0141244 -1.670703
## 45.14286 -1.0141244 -1.670703
## 45.28571 -1.0141244 -1.670703
## 45.42857 -1.0141244 -1.670703
## 45.57143 -1.0141244 -1.670703
## 45.71429 -1.0141244 -1.670703
## 45.85714 -1.0141244 -1.670703
## 
## $秋田県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%     95%
## 44.00000 1.564586 2.21511
## 44.14286 1.466491 2.12307
## 44.28571 1.466491 2.12307
## 44.42857 1.466491 2.12307
## 44.57143 1.466491 2.12307
## 44.71429 1.466491 2.12307
## 44.85714 1.466491 2.12307
## 45.00000 1.466491 2.12307
## 45.14286 1.466491 2.12307
## 45.28571 1.466491 2.12307
## 45.42857 1.466491 2.12307
## 45.57143 1.466491 2.12307
## 45.71429 1.466491 2.12307
## 45.85714 1.466491 2.12307
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 0.6173497 0.7136983 0.8159831 0.8820837 0.7219506 0.7354143 0.7702882
##  [8] 0.8522010 0.8236464 0.7901712 0.7530623 0.7134874 0.6724923 0.6309990
## 
## $山形県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%        95%
## 44.00000 -0.3095736 -0.8002572
## 44.14286 -0.2304046 -0.7301824
## 44.28571 -0.1541897 -0.6677682
## 44.42857 -0.1216333 -0.6529689
## 44.57143 -0.3206027 -0.8724971
## 44.71429 -0.3489050 -0.9229089
## 44.85714 -0.3565600 -0.9530774
## 45.00000 -0.2806223 -0.8803028
## 45.14286 -0.3403828 -0.9565826
## 45.28571 -0.4004060 -1.0306595
## 45.42857 -0.4596105 -1.1015607
## 45.57143 -0.5171626 -1.1686294
## 45.71429 -0.5724351 -1.2314599
## 45.85714 -0.6249709 -1.2898412
## 
## $山形県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 1.544273 2.034957
## 44.14286 1.657801 2.157579
## 44.28571 1.786156 2.299735
## 44.42857 1.885801 2.417136
## 44.57143 1.764504 2.316398
## 44.71429 1.819734 2.393738
## 44.85714 1.897136 2.493654
## 45.00000 1.985024 2.584705
## 45.14286 1.987676 2.603875
## 45.28571 1.980748 2.611002
## 45.42857 1.965735 2.607685
## 45.57143 1.944137 2.595604
## 45.71429 1.917420 2.576444
## 45.85714 1.886969 2.551839
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 1.699016 2.387714 1.771277 1.976862 1.981244 2.123657 3.030424 1.937910
##  [9] 2.832432 2.109970 2.260477 2.201798 2.312316 1.852310
## 
## $福島県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                  80%       95%
## 44.00000 -0.83462341 -2.175851
## 44.14286 -0.21758970 -1.596754
## 44.28571 -0.90377194 -2.319857
## 44.42857 -0.76615857 -2.218226
## 44.57143 -0.82810503 -2.315285
## 44.71429 -0.75048931 -2.271971
## 44.85714  0.09290795 -1.462119
## 45.00000 -1.11406066 -2.729676
## 45.14286 -0.29004852 -1.942990
## 45.28571 -1.08146352 -2.770906
## 45.42857 -0.99844995 -2.723622
## 45.57143 -1.12325324 -2.883429
## 45.71429 -1.07757034 -2.872068
## 45.85714 -1.60119357 -3.429368
## 
## $福島県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 4.232655 5.573883
## 44.14286 4.993017 6.372182
## 44.28571 4.446325 5.862410
## 44.42857 4.719883 6.171950
## 44.57143 4.790593 6.277772
## 44.71429 4.997804 6.519286
## 44.85714 5.967939 7.522966
## 45.00000 4.989881 6.605496
## 45.14286 5.954913 7.607854
## 45.28571 5.301403 6.990846
## 45.42857 5.519405 7.244577
## 45.57143 5.526850 7.287026
## 45.71429 5.702202 7.496700
## 45.85714 5.305815 7.133989
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 8.194239 8.782635 8.483008 8.810415 9.028399 8.522767 8.729853 8.635245
##  [9] 8.635245 8.635245 8.635245 8.635245 8.635245 8.635245
## 
## $茨城県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 4.771770 2.960025
## 44.14286 5.243244 3.369604
## 44.28571 4.830435 2.896880
## 44.42857 5.048064 3.056396
## 44.57143 5.159383 3.111250
## 44.71429 4.549950 2.446867
## 44.85714 4.655877 2.499245
## 45.00000 4.376222 2.121632
## 45.14286 4.262282 1.947375
## 45.28571 4.151236 1.777546
## 45.42857 4.042875 1.611821
## 45.57143 3.937012 1.449918
## 45.71429 3.833483 1.291584
## 45.85714 3.732139 1.136592
## 
## $茨城県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 11.61671 13.42845
## 44.14286 12.32203 14.19567
## 44.28571 12.13558 14.06914
## 44.42857 12.57277 14.56443
## 44.57143 12.89741 14.94555
## 44.71429 12.49558 14.59867
## 44.85714 12.80383 14.96046
## 45.00000 12.89427 15.14886
## 45.14286 13.00821 15.32311
## 45.28571 13.11925 15.49294
## 45.42857 13.22761 15.65867
## 45.57143 13.33348 15.82057
## 45.71429 13.43701 15.97891
## 45.85714 13.53835 16.13390
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 1.599759 1.595012 1.763340 1.779096 1.482905 1.630605 2.176204 1.757934
##  [9] 1.757934 1.757934 1.757934 1.757934 1.757934 1.757934
## 
## $栃木県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -1.393015 -2.977293
## 44.14286 -1.440599 -3.047555
## 44.28571 -1.314513 -2.943830
## 44.42857 -1.340427 -2.991803
## 44.57143 -1.677738 -3.350882
## 44.71429 -1.570631 -3.265263
## 44.85714 -1.065116 -2.780967
## 45.00000 -1.656388 -3.463822
## 45.14286 -1.709418 -3.544923
## 45.28571 -1.761648 -3.624802
## 45.42857 -1.813114 -3.703513
## 45.57143 -1.863849 -3.781106
## 45.71429 -1.913884 -3.857627
## 45.85714 -1.963245 -3.933119
## 
## $栃木県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 4.592532 6.176810
## 44.14286 4.630623 6.237579
## 44.28571 4.841193 6.470510
## 44.42857 4.898619 6.549995
## 44.57143 4.643549 6.316693
## 44.71429 4.831841 6.526473
## 44.85714 5.417525 7.133376
## 45.00000 5.172257 6.979690
## 45.14286 5.225286 7.060791
## 45.28571 5.277516 7.140670
## 45.42857 5.328983 7.219382
## 45.57143 5.379718 7.296974
## 45.71429 5.429752 7.373495
## 45.85714 5.479114 7.448987
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 5.351308 4.132109 4.716394 5.269641 4.972798 4.883705 5.324947 5.417049
##  [9] 5.407384 5.403010 5.528934 5.547409 5.582648 5.582922
## 
## $群馬県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%       95%
## 44.00000  0.8677191 -1.505750
## 44.14286 -1.0032464 -3.721739
## 44.28571 -0.6199338 -3.444815
## 44.42857 -0.1300035 -2.988402
## 44.57143 -0.7480937 -3.776551
## 44.71429 -0.9355361 -4.016056
## 44.85714 -0.6058902 -3.745486
## 45.00000 -0.6206342 -3.816791
## 45.14286 -0.7714052 -4.042258
## 45.28571 -0.8268712 -4.124771
## 45.42857 -0.7666756 -4.099370
## 45.57143 -0.7907293 -4.145937
## 45.71429 -0.8109306 -4.195487
## 45.85714 -0.8411641 -4.241870
## 
## $群馬県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%      95%
## 44.00000  9.834897 12.20837
## 44.14286  9.267465 11.98596
## 44.28571 10.052722 12.87760
## 44.42857 10.669285 13.52768
## 44.57143 10.693690 13.72215
## 44.71429 10.702945 13.78346
## 44.85714 11.255784 14.39538
## 45.00000 11.454732 14.65089
## 45.14286 11.586172 14.85703
## 45.28571 11.632891 14.93079
## 45.42857 11.824545 15.15724
## 45.57143 11.885547 15.24075
## 45.71429 11.976228 15.36078
## 45.85714 12.007009 15.40771
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 61.60240 58.21953 91.79832 72.59432 65.46084 64.36943 72.04164 69.68194
##  [9] 68.13235 85.88032 77.11502 72.02739 73.28254 77.13732
## 
## $埼玉県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 48.38714 41.39141
## 44.14286 44.45158 37.16327
## 44.28571 77.49903 69.92944
## 44.42857 57.78273 49.94195
## 44.57143 50.15410 42.05120
## 44.71429 48.58305 40.22624
## 44.85714 55.78977 47.18655
## 45.00000 51.15302 41.34440
## 45.14286 48.85098 38.64404
## 45.28571 65.87479 55.28450
## 45.42857 56.41065 45.45041
## 45.57143 50.64700 39.32890
## 45.71429 51.24685 39.58187
## 45.85714 54.46528 42.46343
## 
## $埼玉県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000  74.81765  81.81339
## 44.14286  71.98748  79.27579
## 44.28571 106.09761 113.66720
## 44.42857  87.40591  95.24669
## 44.57143  80.76759  88.87049
## 44.71429  80.15582  88.51262
## 44.85714  88.29351  96.89673
## 45.00000  88.21086  98.01948
## 45.14286  87.41371  97.62065
## 45.28571 105.88584 116.47613
## 45.42857  97.81940 108.77963
## 45.57143  93.40778 104.72588
## 45.71429  95.31822 106.98320
## 45.85714  99.80936 111.81122
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 43.20063 48.24817 43.64997 48.50005 44.11026 41.61766 45.73476 44.14164
##  [9] 47.37916 45.48933 47.80870 46.07196 44.74885 46.14284
## 
## $千葉県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 31.79587 25.75856
## 44.14286 35.60192 28.90739
## 44.28571 30.63317 23.74250
## 44.42857 35.12298 28.04158
## 44.57143 30.38237 23.11526
## 44.71429 27.54769 20.09949
## 44.85714 31.33082 23.70584
## 45.00000 28.71066 20.54199
## 45.14286 31.37137 22.89735
## 45.28571 29.04894 20.34592
## 45.42857 30.94680 22.02065
## 45.57143 28.79883 19.65499
## 45.71429 27.07406 17.71760
## 45.85714 28.07533 18.51096
## 
## $千葉県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 54.60539 60.64270
## 44.14286 60.89443 67.58896
## 44.28571 56.66676 63.55744
## 44.42857 61.87712 68.95851
## 44.57143 57.83816 65.10527
## 44.71429 55.68764 63.13584
## 44.85714 60.13869 67.76368
## 45.00000 59.57262 67.74129
## 45.14286 63.38695 71.86096
## 45.28571 61.92973 70.63275
## 45.42857 64.67060 73.59675
## 45.57143 63.34508 72.48892
## 45.71429 62.42363 71.78010
## 45.85714 64.21036 73.77473
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 230.9699 312.1612 282.0365 313.9466 213.4114 174.2899 278.1615 234.9435
##  [9] 322.0280 291.5630 323.1444 222.2920 182.8642 286.4401
## 
## $東京都$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 185.7464 161.80656
## 44.14286 260.4646 233.09803
## 44.28571 227.5591 198.72053
## 44.42857 256.9991 226.85291
## 44.57143 154.2541 122.93809
## 44.71429 113.1444  80.77600
## 44.85714 215.2193 181.89970
## 45.00000 164.9938 127.96474
## 45.14286 248.4451 209.49264
## 45.28571 215.4256 175.12094
## 45.42857 244.7007 203.17508
## 45.57143 141.7577  99.12538
## 45.71429 100.4287  56.79006
## 45.85714 202.2711 157.71475
## 
## $東京都$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 276.1934 300.1333
## 44.14286 363.8579 391.2245
## 44.28571 336.5139 365.3525
## 44.42857 370.8941 401.0403
## 44.57143 272.5687 303.8847
## 44.71429 235.4353 267.8037
## 44.85714 341.1038 374.4234
## 45.00000 304.8931 341.9222
## 45.14286 395.6109 434.5634
## 45.28571 367.7003 408.0050
## 45.42857 401.5882 443.1138
## 45.57143 302.8263 345.4586
## 45.71429 265.2996 308.9383
## 45.85714 370.6091 415.1655
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1]  72.56608 100.10603  92.51120 108.24425  83.00440  66.11345  90.97162
##  [8]  80.66047  92.70180  91.78045  97.93621  84.97929  79.26364  88.83308
## 
## $神奈川県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 52.85963 42.42766
## 44.14286 78.34122 66.81963
## 44.28571 70.09136 58.22301
## 44.42857 85.47792 73.42615
## 44.57143 59.98081 47.79286
## 44.71429 42.86318 30.55523
## 44.85714 67.50659 55.08496
## 45.00000 54.45129 40.57699
## 45.14286 65.53942 51.16052
## 45.28571 64.07520 49.40893
## 45.42857 69.81136 54.92296
## 45.57143 56.47874 41.39146
## 45.71429 50.40557 35.12903
## 45.85714 59.62666 44.16571
## 
## $神奈川県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%      95%
## 44.00000  92.27253 102.7045
## 44.14286 121.87084 133.3924
## 44.28571 114.93104 126.7994
## 44.42857 131.01059 143.0624
## 44.57143 106.02799 118.2159
## 44.71429  89.36372 101.6717
## 44.85714 114.43664 126.8583
## 45.00000 106.86965 120.7439
## 45.14286 119.86419 134.2431
## 45.28571 119.48571 134.1520
## 45.42857 126.06107 140.9495
## 45.57143 113.47984 128.5671
## 45.71429 108.12171 123.3982
## 45.85714 118.03951 133.5005
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 1.8826223 1.9238762 1.5968733 1.5710784 2.2348090 1.5101543 1.2709055
##  [8] 1.1835899 1.0900622 1.0141769 0.9526061 0.9026497 0.8621168 0.8292297
## 
## $新潟県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                  80%        95%
## 44.00000  0.44925355 -0.3095260
## 44.14286  0.44353318 -0.3401131
## 44.28571  0.08640362 -0.7131908
## 44.42857  0.04109955 -0.7688223
## 44.57143  0.69212175 -0.1245276
## 44.71429 -0.04084233 -0.8618903
## 44.85714 -0.28553702 -1.1094679
## 45.00000 -0.40666126 -1.2484894
## 45.14286 -0.50656817 -1.3517732
## 45.28571 -0.58663905 -1.4340598
## 45.42857 -0.65095928 -1.4998355
## 45.57143 -0.70272316 -1.5525562
## 45.71429 -0.74444488 -1.5949072
## 45.85714 -0.77811405 -1.6289904
## 
## $新潟県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 3.315991 4.074771
## 44.14286 3.404219 4.187866
## 44.28571 3.107343 3.906937
## 44.42857 3.101057 3.910979
## 44.57143 3.777496 4.594146
## 44.71429 3.061151 3.882199
## 44.85714 2.827348 3.651279
## 45.00000 2.773841 3.615669
## 45.14286 2.686692 3.531898
## 45.28571 2.614993 3.462414
## 45.42857 2.556172 3.405048
## 45.57143 2.508023 3.357856
## 45.71429 2.468678 3.319141
## 45.85714 2.436573 3.287450
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 0.5023054 0.5895997 0.6075066 0.7481285 0.7250675 0.7698430 0.7981017
##  [8] 0.8400621 0.8624173 0.8913251 0.9162239 0.9415202 0.9633596 0.9847999
## 
## $富山県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -1.969022 -3.277264
## 44.14286 -2.015231 -3.394145
## 44.28571 -2.148312 -3.607154
## 44.42857 -2.119155 -3.637004
## 44.57143 -2.313332 -3.921764
## 44.71429 -2.366627 -4.026974
## 44.85714 -2.428468 -4.136511
## 45.00000 -2.461332 -4.208985
## 45.14286 -2.509159 -4.293964
## 45.28571 -2.537473 -4.352569
## 45.42857 -2.562913 -4.404657
## 45.57143 -2.581049 -4.445784
## 45.71429 -2.597480 -4.482475
## 45.85714 -2.609126 -4.511635
## 
## $富山県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 2.973633 4.281874
## 44.14286 3.194431 4.573345
## 44.28571 3.363325 4.822167
## 44.42857 3.615412 5.133261
## 44.57143 3.763467 5.371899
## 44.71429 3.906313 5.566660
## 44.85714 4.024671 5.732714
## 45.00000 4.141457 5.889109
## 45.14286 4.233994 6.018799
## 45.28571 4.320123 6.135219
## 45.42857 4.395361 6.237105
## 45.57143 4.464089 6.328825
## 45.71429 4.524200 6.409194
## 45.85714 4.578726 6.481235
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 1.525474 1.525474 1.525474 1.525474 1.525474 1.525474 1.525474 1.525474
##  [9] 1.525474 1.525474 1.525474 1.525474 1.525474 1.525474
## 
## $石川県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -2.178847 -4.139796
## 44.14286 -2.387147 -4.458364
## 44.28571 -2.584905 -4.760809
## 44.42857 -2.773576 -5.049356
## 44.57143 -2.954308 -5.325761
## 44.71429 -3.128026 -5.591440
## 44.85714 -3.295488 -5.847551
## 45.00000 -3.457325 -6.095060
## 45.14286 -3.614069 -6.334778
## 45.28571 -3.766172 -6.567400
## 45.42857 -3.914023 -6.793519
## 45.57143 -4.057961 -7.013652
## 45.71429 -4.198280 -7.228252
## 45.85714 -4.335240 -7.437714
## 
## $石川県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 5.229794  7.190743
## 44.14286 5.438095  7.509312
## 44.28571 5.635853  7.811757
## 44.42857 5.824524  8.100304
## 44.57143 6.005256  8.376709
## 44.71429 6.178974  8.642387
## 44.85714 6.346436  8.898499
## 45.00000 6.508273  9.146007
## 45.14286 6.665017  9.385726
## 45.28571 6.817119  9.618347
## 45.42857 6.964971  9.844467
## 45.57143 7.108908 10.064600
## 45.71429 7.249227 10.279199
## 45.85714 7.386188 10.488662
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 2.880915 3.208741 2.887559 2.611569 2.374411 2.170621 1.995506 1.845029
##  [9] 1.715725 1.604615 1.509138 1.427095 1.356595 1.296015
## 
## $福井県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%          95%
## 44.00000  0.9922552 -0.007541056
## 44.14286  1.2268802  0.177746302
## 44.28571  0.6603606 -0.518647166
## 44.42857  0.2193086 -1.047077323
## 44.57143 -0.1327646 -1.459982941
## 44.71429 -0.4181345 -1.788538948
## 44.85714 -0.6518755 -2.053314441
## 45.00000 -0.8448205 -2.268740963
## 45.14286 -1.0050575 -2.445352962
## 45.28571 -1.1387850 -2.591053046
## 45.42857 -1.2508431 -2.711888765
## 45.57143 -1.3450660 -2.812559223
## 45.71429 -1.4245247 -2.896760579
## 45.85714 -1.4917016 -2.967429683
## 
## $福井県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 4.769575 5.769372
## 44.14286 5.190602 6.239736
## 44.28571 5.114758 6.293766
## 44.42857 5.003829 6.270215
## 44.57143 4.881586 6.208804
## 44.71429 4.759377 6.129781
## 44.85714 4.642887 6.044326
## 45.00000 4.534879 5.958799
## 45.14286 4.436508 5.876804
## 45.28571 4.348014 5.800283
## 45.42857 4.269119 5.730164
## 45.57143 4.199255 5.666748
## 45.71429 4.137715 5.609951
## 45.85714 4.083732 5.559460
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 3.592544 4.240809 3.675035 3.859279 3.859279 3.859279 3.859279 3.859279
##  [9] 3.859279 3.859279 3.859279 3.859279 3.859279 3.859279
## 
## $山梨県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 2.043373 1.2232918
## 44.14286 2.554382 1.6616419
## 44.28571 1.922645 0.9949863
## 44.42857 2.089164 1.1521223
## 44.57143 2.046495 1.0868651
## 44.71429 2.004807 1.0231091
## 44.85714 1.964036 0.9607550
## 45.00000 1.924124 0.8997144
## 45.14286 1.885018 0.8399076
## 45.28571 1.846672 0.7812626
## 45.42857 1.809043 0.7237142
## 45.57143 1.772093 0.6672032
## 45.71429 1.735785 0.6116754
## 45.85714 1.700088 0.5570812
## 
## $山梨県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 5.141714 5.961795
## 44.14286 5.927235 6.819975
## 44.28571 5.427425 6.355085
## 44.42857 5.629393 6.566435
## 44.57143 5.672062 6.631692
## 44.71429 5.713750 6.695448
## 44.85714 5.754521 6.757802
## 45.00000 5.794434 6.818843
## 45.14286 5.833539 6.878650
## 45.28571 5.871885 6.937295
## 45.42857 5.909514 6.994843
## 45.57143 5.946464 7.051354
## 45.71429 5.982772 7.106882
## 45.85714 6.018469 7.161476
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 8.756869 5.487441 7.919584 6.110301 7.456235 6.454989 7.199821 6.645737
##  [9] 7.057922 6.751295 6.979397 6.809711 6.935941 6.842038
## 
## $長野県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 6.027736 4.5830203
## 44.14286 2.687808 1.2057717
## 44.28571 4.891225 3.2881082
## 44.42857 2.988005 1.3351615
## 44.57143 4.160982 2.4165805
## 44.71429 3.055525 1.2559576
## 44.85714 3.656990 1.7815290
## 45.00000 2.996010 1.0639607
## 45.14286 3.282338 1.2836651
## 45.28571 2.869958 0.8153019
## 45.42857 2.983586 0.8683320
## 45.57143 2.711012 0.5412924
## 45.71429 2.730807 0.5047443
## 45.85714 2.537518 0.2588431
## 
## $長野県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 11.486001 12.930717
## 44.14286  8.287074  9.769111
## 44.28571 10.947943 12.551059
## 44.42857  9.232598 10.885441
## 44.57143 10.751488 12.495889
## 44.71429  9.854452 11.654020
## 44.85714 10.742651 12.618112
## 45.00000 10.295464 12.227513
## 45.14286 10.833506 12.832179
## 45.28571 10.632633 12.687289
## 45.42857 10.975207 13.090461
## 45.57143 10.908410 13.078130
## 45.71429 11.141074 13.367137
## 45.85714 11.146558 13.425233
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1]  8.561625 10.653066 11.350990  7.457094  7.511993  7.894849  9.127858
##  [8]  7.257154  7.978663  8.588632  5.905191  6.341811  6.046990  6.944217
## 
## $岐阜県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%         95%
## 44.00000 5.2758449  3.53645792
## 44.14286 6.9042511  4.91974829
## 44.28571 7.1825515  4.97591338
## 44.42857 3.0386551  0.69967455
## 44.57143 2.9126419  0.47789247
## 44.71429 3.1686681  0.66677900
## 44.85714 4.3093199  1.75853990
## 45.00000 2.1285139 -0.58642414
## 45.14286 2.7056474 -0.08571824
## 45.28571 3.1932572  0.33711812
## 45.42857 0.4239668 -2.47761819
## 45.57143 0.7949266 -2.14141665
## 45.71429 0.4502740 -2.51244857
## 45.85714 1.3087558 -1.67447705
## 
## $岐阜県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 11.84741 13.58679
## 44.14286 14.40188 16.38638
## 44.28571 15.51943 17.72607
## 44.42857 11.87553 14.21451
## 44.57143 12.11134 14.54609
## 44.71429 12.62103 15.12292
## 44.85714 13.94640 16.49718
## 45.00000 12.38579 15.10073
## 45.14286 13.25168 16.04304
## 45.28571 13.98401 16.84015
## 45.42857 11.38642 14.28800
## 45.57143 11.88870 14.82504
## 45.71429 11.64371 14.60643
## 45.85714 12.57968 15.56291
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 18.56691 15.75337 14.13485 15.79930 13.82251 13.12592 16.56104 14.11182
##  [9] 14.94353 16.80210 18.93139 15.64699 14.44687 16.09391
## 
## $静岡県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 14.714067 12.674493
## 44.14286 11.293507  8.932598
## 44.28571  9.405034  6.901223
## 44.42857 10.916157  8.331178
## 44.57143  8.574064  5.795706
## 44.71429  7.633663  4.726237
## 44.85714 10.895540  7.896404
## 45.00000  8.210740  5.086898
## 45.14286  8.839125  5.607647
## 45.28571 10.529405  7.208841
## 45.42857 12.446269  9.013251
## 45.57143  8.974725  5.442641
## 45.71429  7.608921  3.989131
## 45.85714  9.070895  5.353134
## 
## $静岡県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 22.41976 24.45934
## 44.14286 20.21323 22.57414
## 44.28571 18.86466 21.36847
## 44.42857 20.68244 23.26742
## 44.57143 19.07095 21.84931
## 44.71429 18.61818 21.52561
## 44.85714 22.22655 25.22569
## 45.00000 20.01290 23.13674
## 45.14286 21.04794 24.27942
## 45.28571 23.07480 26.39536
## 45.42857 25.41652 28.84954
## 45.57143 22.31925 25.85134
## 45.71429 21.28481 24.90460
## 45.85714 23.11693 26.83469
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 106.76527 107.38154 119.51589 118.52519 105.39757  95.14444 116.71732
##  [8] 115.41466 106.63345 109.69438 115.06911  96.84231  97.95059 105.47729
## 
## $愛知県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 92.64307 85.16723
## 44.14286 89.83815 80.55125
## 44.28571 98.69401 87.67157
## 44.42857 95.01792 82.57393
## 44.57143 80.48729 67.30059
## 44.71429 68.23340 53.98756
## 44.85714 88.68462 73.84501
## 45.00000 83.89047 67.20257
## 45.14286 72.35089 54.20279
## 45.28571 73.58719 54.47321
## 45.42857 76.82715 56.58308
## 45.57143 57.24221 36.27918
## 45.71429 57.06504 35.42154
## 45.85714 63.29666 40.96759
## 
## $愛知県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 120.8875 128.3633
## 44.14286 124.9249 134.2118
## 44.28571 140.3378 151.3602
## 44.42857 142.0325 154.4764
## 44.57143 130.3078 143.4945
## 44.71429 122.0555 136.3013
## 44.85714 144.7500 159.5896
## 45.00000 146.9389 163.6268
## 45.14286 140.9160 159.0641
## 45.28571 145.8016 164.9156
## 45.42857 153.3111 173.5551
## 45.57143 136.4424 157.4054
## 45.71429 138.8361 160.4796
## 45.85714 147.6579 169.9870
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 2.886726 2.886726 2.886726 2.886726 2.886726 2.886726 2.886726 2.886726
##  [9] 2.886726 2.886726 2.886726 2.886726 2.886726 2.886726
## 
## $三重県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%       95%
## 44.00000 -0.8287792 -2.795649
## 44.14286 -1.0357345 -3.112160
## 44.28571 -1.2323046 -3.412788
## 44.42857 -1.4199120 -3.699709
## 44.57143 -1.5996810 -3.974642
## 44.71429 -1.7725191 -4.238975
## 44.85714 -1.9391710 -4.493847
## 45.00000 -2.1002570 -4.740206
## 45.14286 -2.2563000 -4.978854
## 45.28571 -2.4077460 -5.210470
## 45.42857 -2.5549788 -5.435643
## 45.57143 -2.6983316 -5.654883
## 45.71429 -2.8380959 -5.868634
## 45.85714 -2.9745284 -6.077289
## 
## $三重県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 6.602232  8.569102
## 44.14286 6.809187  8.885612
## 44.28571 7.005757  9.186241
## 44.42857 7.193365  9.473161
## 44.57143 7.373134  9.748094
## 44.71429 7.545972 10.012427
## 44.85714 7.712624 10.267299
## 45.00000 7.873710 10.513659
## 45.14286 8.029753 10.752306
## 45.28571 8.181199 10.983923
## 45.42857 8.328431 11.209096
## 45.57143 8.471784 11.428335
## 45.71429 8.611549 11.642086
## 45.85714 8.747981 11.850742
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 11.643855  6.984615  9.141701  7.589156  8.288078  7.770029  7.995950
##  [8]  7.822828  7.895652  7.837702  7.861099  7.841666  7.849154  7.842624
## 
## $滋賀県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%         95%
## 44.00000 7.786247  5.74415308
## 44.14286 3.064291  0.98899764
## 44.28571 4.770011  2.45577887
## 44.42857 3.130996  0.77098824
## 44.57143 3.653044  1.19940581
## 44.71429 3.046268  0.54565993
## 44.85714 3.159826  0.59973655
## 45.00000 2.900711  0.29509914
## 45.14286 2.881646  0.22739223
## 45.28571 2.740923  0.04285178
## 45.42857 2.680675 -0.06167597
## 45.57143 2.581338 -0.20331165
## 45.71429 2.509462 -0.31720027
## 45.85714 2.425523 -0.44211801
## 
## $滋賀県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 15.50146 17.54356
## 44.14286 10.90494 12.98023
## 44.28571 13.51339 15.82762
## 44.42857 12.04732 14.40732
## 44.57143 12.92311 15.37675
## 44.71429 12.49379 14.99440
## 44.85714 12.83207 15.39216
## 45.00000 12.74495 15.35056
## 45.14286 12.90966 15.56391
## 45.28571 12.93448 15.63255
## 45.42857 13.04152 15.78387
## 45.57143 13.10199 15.88664
## 45.71429 13.18885 16.01551
## 45.85714 13.25973 16.12737
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 13.63320 13.81311 16.44877 16.57408 16.34486 13.29301 14.79902 14.05113
##  [9] 14.41454 16.13843 15.54473 15.76929 14.17862 14.70524
## 
## $京都府$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 7.296696 3.9423537
## 44.14286 6.879386 3.2088962
## 44.28571 9.287926 5.4972058
## 44.42857 9.259815 5.3878770
## 44.57143 8.897226 4.9546862
## 44.71429 5.719011 1.7095771
## 44.85714 7.102051 3.0275199
## 45.00000 5.988426 1.7202853
## 45.14286 6.147569 1.7712991
## 45.28571 7.706647 3.2431306
## 45.42857 6.960570 2.4163934
## 45.57143 7.038033 2.4159839
## 45.71429 5.303439 0.6052006
## 45.85714 5.688644 0.9155457
## 
## $京都府$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 19.96970 23.32405
## 44.14286 20.74683 24.41732
## 44.28571 23.60961 27.40033
## 44.42857 23.88834 27.76028
## 44.57143 23.79249 27.73503
## 44.71429 20.86701 24.87644
## 44.85714 22.49600 26.57053
## 45.00000 22.11384 26.38198
## 45.14286 22.68151 27.05778
## 45.28571 24.57022 29.03373
## 45.42857 24.12888 28.67306
## 45.57143 24.50055 29.12260
## 45.71429 23.05380 27.75204
## 45.85714 23.72184 28.49493
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 152.1610 173.0346 196.4946 208.3847 178.9401 136.3124 227.8949 171.9172
##  [9] 194.1444 217.2691 229.6975 201.5857 163.8325 247.1260
## 
## $大阪府$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 128.8457 116.50329
## 44.14286 147.8112 134.45877
## 44.28571 169.4976 155.20634
## 44.42857 179.7238 164.55160
## 44.57143 148.7066 132.70191
## 44.71429 104.5841  87.78821
## 44.85714 194.7393 177.18775
## 45.00000 133.1723 112.66205
## 45.14286 152.9729 131.17795
## 45.28571 173.8061 150.79822
## 45.42857 184.0581 159.89800
## 45.57143 153.8689 128.60921
## 45.71429 114.1251  87.81166
## 45.85714 195.5047 168.17809
## 
## $大阪府$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 175.4763 187.8186
## 44.14286 198.2580 211.6104
## 44.28571 223.4915 237.7828
## 44.42857 237.0457 252.2179
## 44.57143 209.1736 225.1782
## 44.71429 168.0406 184.8365
## 44.85714 261.0506 278.6021
## 45.00000 210.6621 231.1724
## 45.14286 235.3160 257.1109
## 45.28571 260.7320 283.7399
## 45.42857 275.3370 299.4970
## 45.57143 249.3025 274.5622
## 45.71429 213.5399 239.8534
## 45.85714 298.7472 326.0739
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 42.41799 46.41974 49.03117 39.41693 41.59997 36.51598 51.60857 44.36381
##  [9] 46.01176 46.71302 44.14273 44.69368 43.30032 46.08134
## 
## $兵庫県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 33.46547 28.72630
## 44.14286 37.02863 32.05728
## 44.28571 39.22106 34.02790
## 44.42857 29.20499 23.79912
## 44.57143 31.00144 25.39091
## 44.71429 25.54446 19.73648
## 44.85714 40.27632 34.27740
## 45.00000 31.05064 24.00307
## 45.14286 32.06630 24.68402
## 45.28571 32.16273 24.46027
## 45.42857 29.01176 21.00191
## 45.57143 29.00351 20.69764
## 45.71429 27.07021 18.47851
## 45.85714 29.32868 20.46036
## 
## $兵庫県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 51.37051 56.10968
## 44.14286 55.81085 60.78220
## 44.28571 58.84128 64.03444
## 44.42857 49.62886 55.03473
## 44.57143 52.19851 57.80904
## 44.71429 47.48751 53.29548
## 44.85714 62.94081 68.93973
## 45.00000 57.67699 64.72456
## 45.14286 59.95722 67.33950
## 45.28571 61.26332 68.96578
## 45.42857 59.27370 67.28355
## 45.57143 60.38384 68.68972
## 45.71429 59.53043 68.12214
## 45.85714 62.83401 71.70233
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 8.676403 8.969426 8.896531 8.984947 8.971135 8.998767 8.997416 9.006397
##  [9] 9.006996 9.010036 9.010588 9.011657 9.011965 9.012354
## 
## $奈良県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 4.985539 3.0317133
## 44.14286 5.156039 3.1373535
## 44.28571 4.653122 2.4067972
## 44.42857 4.610432 2.2947036
## 44.57143 4.416434 2.0053212
## 44.71429 4.329424 1.8576233
## 44.85714 4.207419 1.6717474
## 45.00000 4.114437 1.5247897
## 45.14286 4.014254 1.3712553
## 45.28571 3.923192 1.2303799
## 45.42857 3.831601 1.0900111
## 45.57143 3.743637 0.9549158
## 45.71429 3.656640 0.8217030
## 45.85714 3.571632 0.6914871
## 
## $奈良県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 12.36727 14.32109
## 44.14286 12.78281 14.80150
## 44.28571 13.13994 15.38626
## 44.42857 13.35946 15.67519
## 44.57143 13.52584 15.93695
## 44.71429 13.66811 16.13991
## 44.85714 13.78741 16.32309
## 45.00000 13.89836 16.48800
## 45.14286 13.99974 16.64274
## 45.28571 14.09688 16.78969
## 45.42857 14.18957 16.93116
## 45.57143 14.27968 17.06840
## 45.71429 14.36729 17.20223
## 45.85714 14.45308 17.33322
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 1.8350043 1.6519646 1.9900721 0.9511945 1.6746742 1.7982551 1.6809983
##  [8] 1.6775583 1.6796345 1.6796345 1.6796345 1.6796345 1.6796345 1.6796345
## 
## $和歌山県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                  80%       95%
## 44.00000 -0.03017870 -1.017547
## 44.14286 -0.34516596 -1.402383
## 44.28571 -0.06871719 -1.158574
## 44.42857 -1.16745995 -2.289008
## 44.57143 -0.50219964 -1.654567
## 44.71429 -0.43532113 -1.617705
## 44.85714 -0.60787610 -1.819533
## 45.00000 -0.61815915 -1.833439
## 45.14286 -0.64674702 -1.878259
## 45.28571 -0.68320853 -1.934022
## 45.42857 -0.71911588 -1.988938
## 45.57143 -0.75449359 -2.043043
## 45.71429 -0.78936444 -2.096374
## 45.85714 -0.82374960 -2.148961
## 
## $和歌山県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 3.700187 4.687556
## 44.14286 3.649095 4.706312
## 44.28571 4.048861 5.138719
## 44.42857 3.069849 4.191397
## 44.57143 3.851548 5.003915
## 44.71429 4.031831 5.214215
## 44.85714 3.969873 5.181530
## 45.00000 3.973276 5.188555
## 45.14286 4.006016 5.237528
## 45.28571 4.042477 5.293291
## 45.42857 4.078385 5.348207
## 45.57143 4.113763 5.402312
## 45.71429 4.148633 5.455642
## 45.85714 4.183019 5.508230
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 1.8227112 1.8229985 1.3481200 1.7035607 1.7715611 0.9925333 1.6787080
##  [8] 1.6066795 1.6067491 1.2020249 1.5053638 1.5834086 0.8986231 1.4939365
## 
## $鳥取県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                  80%          95%
## 44.00000  0.87477774  0.372972060
## 44.14286  0.85671739  0.345199067
## 44.28571  0.36383325 -0.157216699
## 44.42857  0.70159170  0.171181383
## 44.57143  0.75221668  0.212608334
## 44.71429 -0.04389538 -0.592547566
## 44.85714  0.62547217  0.067922817
## 45.00000  0.55077962 -0.008180016
## 45.14286  0.53800796 -0.027749430
## 45.28571  0.12059493 -0.451879496
## 45.42857  0.41139228 -0.167721283
## 45.57143  0.47703755 -0.108639899
## 45.71429 -0.22000994 -0.812178518
## 45.85714  0.36317437 -0.235414947
## 
## $鳥取県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 2.770645 3.272450
## 44.14286 2.789280 3.300798
## 44.28571 2.332407 2.853457
## 44.42857 2.705530 3.235940
## 44.57143 2.790906 3.330514
## 44.71429 2.028962 2.577614
## 44.85714 2.731944 3.289493
## 45.00000 2.662579 3.221539
## 45.14286 2.675490 3.241248
## 45.28571 2.283455 2.855929
## 45.42857 2.599335 3.178449
## 45.57143 2.689780 3.275457
## 45.71429 2.017256 2.609425
## 45.85714 2.624699 3.223288
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 0.4684385 0.4684385 0.4684385 0.4684385 0.4684385 0.4684385 0.4684385
##  [8] 0.4684385 0.4684385 0.4684385 0.4684385 0.4684385 0.4684385 0.4684385
## 
## $島根県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -6.358335 -9.972211
## 44.14286 -6.358335 -9.972211
## 44.28571 -6.358335 -9.972211
## 44.42857 -6.358335 -9.972211
## 44.57143 -6.358335 -9.972211
## 44.71429 -6.358335 -9.972211
## 44.85714 -6.358335 -9.972211
## 45.00000 -6.358335 -9.972211
## 45.14286 -6.358335 -9.972211
## 45.28571 -6.358335 -9.972211
## 45.42857 -6.358335 -9.972211
## 45.57143 -6.358335 -9.972211
## 45.71429 -6.358335 -9.972211
## 45.85714 -6.358335 -9.972211
## 
## $島根県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 7.295212 10.90909
## 44.14286 7.295212 10.90909
## 44.28571 7.295212 10.90909
## 44.42857 7.295212 10.90909
## 44.57143 7.295212 10.90909
## 44.71429 7.295212 10.90909
## 44.85714 7.295212 10.90909
## 45.00000 7.295212 10.90909
## 45.14286 7.295212 10.90909
## 45.28571 7.295212 10.90909
## 45.42857 7.295212 10.90909
## 45.57143 7.295212 10.90909
## 45.71429 7.295212 10.90909
## 45.85714 7.295212 10.90909
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1]  1.460569 -2.129994  3.580284  3.370010  5.307554  5.996629  3.071461
##  [8]  4.510570  5.584523  4.319133  4.269290  4.671050  5.642860  5.877410
## 
## $岡山県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%         95%
## 44.00000 -1.1046517 -2.46259767
## 44.14286 -4.9916682 -6.50654691
## 44.28571  0.6022176 -0.97427547
## 44.42857  0.3169654 -1.29921857
## 44.57143  2.1921069  0.54288878
## 44.71429  2.8234357  1.14364834
## 44.85714 -0.1573148 -1.86652557
## 45.00000  1.1482108 -0.63171471
## 45.14286  2.1365103  0.31124262
## 45.28571  0.7999844 -1.06294032
## 45.42857  0.6843727 -1.21336801
## 45.57143  1.0228534 -0.90838511
## 45.71429  1.9329006 -0.03103334
## 45.85714  2.1068452  0.11082886
## 
## $岡山県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%       95%
## 44.00000 4.025790  5.383736
## 44.14286 0.731680  2.246559
## 44.28571 6.558350  8.134843
## 44.42857 6.423054  8.039238
## 44.57143 8.423001 10.072219
## 44.71429 9.169823 10.849611
## 44.85714 6.300237  8.009448
## 45.00000 7.872929  9.652855
## 45.14286 9.032536 10.857803
## 45.28571 7.838281  9.701206
## 45.42857 7.854208  9.751949
## 45.57143 8.319246 10.250484
## 45.71429 9.352820 11.316754
## 45.85714 9.647974 11.643991
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 2.398628 2.162422 2.000805 2.160498 2.212472 2.116652 2.109363 2.161792
##  [9] 2.154549 2.128243 2.137665 2.149638 2.142096 2.137337
## 
## $広島県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -1.135048 -3.005663
## 44.14286 -1.757944 -3.833260
## 44.28571 -2.111349 -4.288191
## 44.42857 -2.557483 -5.055031
## 44.57143 -3.048689 -5.833779
## 44.71429 -3.429765 -6.365860
## 44.85714 -3.743502 -6.841822
## 45.00000 -4.073123 -7.373687
## 45.14286 -4.402366 -7.873387
## 45.28571 -4.700130 -8.314852
## 45.42857 -4.976388 -8.742340
## 45.57143 -5.251757 -9.169819
## 45.71429 -5.520818 -9.577320
## 45.85714 -5.775330 -9.964043
## 
## $広島県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000  5.932305  7.802920
## 44.14286  6.082788  8.158105
## 44.28571  6.112958  8.289800
## 44.42857  6.878478  9.376026
## 44.57143  7.473633 10.258723
## 44.71429  7.663068 10.599164
## 44.85714  7.962227 11.060547
## 45.00000  8.396707 11.697272
## 45.14286  8.711464 12.182486
## 45.28571  8.956615 12.571337
## 45.42857  9.251718 13.017670
## 45.57143  9.551032 13.469094
## 45.71429  9.805010 13.861512
## 45.85714 10.050004 14.238717
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 2.380916 2.380916 2.380916 2.380916 2.380916 2.380916 2.380916 2.380916
##  [9] 2.380916 2.380916 2.380916 2.380916 2.380916 2.380916
## 
## $山口県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                  80%        95%
## 44.00000  0.45798985 -0.5599456
## 44.14286  0.38405782 -0.6730149
## 44.28571  0.31276702 -0.7820448
## 44.42857  0.24385310 -0.8874395
## 44.57143  0.17709308 -0.9895402
## 44.71429  0.11229680 -1.0886375
## 44.85714  0.04930053 -1.1849820
## 45.00000 -0.01203789 -1.2787910
## 45.14286 -0.07184284 -1.3702548
## 45.28571 -0.13022389 -1.4595409
## 45.42857 -0.18727814 -1.5467979
## 45.57143 -0.24309215 -1.6321580
## 45.71429 -0.29774344 -1.7157400
## 45.85714 -0.35130178 -1.7976504
## 
## $山口県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 4.303842 5.321777
## 44.14286 4.377774 5.434846
## 44.28571 4.449064 5.543876
## 44.42857 4.517978 5.649271
## 44.57143 4.584738 5.751372
## 44.71429 4.649535 5.850469
## 44.85714 4.712531 5.946814
## 45.00000 4.773869 6.040623
## 45.14286 4.833674 6.132086
## 45.28571 4.892055 6.221372
## 45.42857 4.949110 6.308629
## 45.57143 5.004924 6.393990
## 45.71429 5.059575 6.477571
## 45.85714 5.113133 6.559482
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 0.9403658 0.7582955 1.3071027 0.2808193 0.6753327 0.8489672 0.9661008
##  [8] 0.8421150 0.7991215 0.3687293 0.9289718 0.8322224 0.6865285 0.5551527
## 
## $徳島県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%       95%
## 44.00000 -0.7960966 -1.715324
## 44.14286 -1.0615266 -2.024882
## 44.28571 -0.5316806 -1.505074
## 44.42857 -1.5767315 -2.560059
## 44.57143 -1.2007978 -2.193961
## 44.71429 -1.0455610 -2.048464
## 44.85714 -0.9466480 -1.959196
## 45.00000 -1.1697830 -2.234817
## 45.14286 -1.2476823 -2.331195
## 45.28571 -1.7026379 -2.799153
## 45.42857 -1.1666710 -2.276037
## 45.57143 -1.2874179 -2.409488
## 45.71429 -1.4568407 -2.591472
## 45.85714 -1.6116855 -2.758740
## 
## $徳島県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 2.676828 3.596056
## 44.14286 2.578118 3.541473
## 44.28571 3.145886 4.119279
## 44.42857 2.138370 3.121698
## 44.57143 2.551463 3.544627
## 44.71429 2.743495 3.746398
## 44.85714 2.878850 3.891398
## 45.00000 2.854013 3.919048
## 45.14286 2.845925 3.929438
## 45.28571 2.440096 3.536612
## 45.42857 3.024615 4.133981
## 45.57143 2.951863 4.073933
## 45.71429 2.829898 3.964529
## 45.85714 2.721991 3.869046
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 0.6217277 0.4483513 0.4483513 0.4483513 0.4483513 0.4483513 0.4483513
##  [8] 0.4483513 0.4483513 0.4483513 0.4483513 0.4483513 0.4483513 0.4483513
## 
## $香川県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%       95%
## 44.00000 -0.7403155 -1.461338
## 44.14286 -0.9731075 -1.725582
## 44.28571 -0.9770143 -1.731557
## 44.42857 -0.9809104 -1.737516
## 44.57143 -0.9847960 -1.743458
## 44.71429 -0.9886710 -1.749385
## 44.85714 -0.9925355 -1.755295
## 45.00000 -0.9963898 -1.761190
## 45.14286 -1.0002338 -1.767068
## 45.28571 -1.0040676 -1.772932
## 45.42857 -1.0078913 -1.778780
## 45.57143 -1.0117051 -1.784612
## 45.71429 -1.0155088 -1.790430
## 45.85714 -1.0193028 -1.796232
## 
## $香川県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 1.983771 2.704793
## 44.14286 1.869810 2.622285
## 44.28571 1.873717 2.628260
## 44.42857 1.877613 2.634219
## 44.57143 1.881499 2.640161
## 44.71429 1.885374 2.646087
## 44.85714 1.889238 2.651998
## 45.00000 1.893092 2.657892
## 45.14286 1.896936 2.663771
## 45.28571 1.900770 2.669634
## 45.42857 1.904594 2.675482
## 45.57143 1.908408 2.681315
## 45.71429 1.912211 2.687132
## 45.85714 1.916005 2.692934
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 0.1708210 0.3352266 0.4083756 0.3721322 0.3157910 0.3728840 0.3217238
##  [8] 0.3889015 0.3631217 0.3997987 0.3703190 0.3992035 0.3730023 0.3974666
## 
## $愛媛県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -1.459595 -2.322686
## 44.14286 -1.342090 -2.230008
## 44.28571 -1.296226 -2.198587
## 44.42857 -1.333106 -2.235805
## 44.57143 -1.394067 -2.299211
## 44.71429 -1.339206 -2.245532
## 44.85714 -1.392789 -2.300398
## 45.00000 -1.328659 -2.237881
## 45.14286 -1.358651 -2.270103
## 45.28571 -1.322350 -2.234000
## 45.42857 -1.352868 -2.265069
## 45.57143 -1.324601 -2.237128
## 45.71429 -1.351415 -2.264266
## 45.85714 -1.327449 -2.240564
## 
## $愛媛県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 1.801237 2.664328
## 44.14286 2.012543 2.900461
## 44.28571 2.112977 3.015338
## 44.42857 2.077370 2.980069
## 44.57143 2.025649 2.930793
## 44.71429 2.084974 2.991300
## 44.85714 2.036237 2.943845
## 45.00000 2.106462 3.015684
## 45.14286 2.084894 2.996346
## 45.28571 2.121947 3.033597
## 45.42857 2.093506 3.005707
## 45.57143 2.123008 3.035535
## 45.71429 2.097420 3.010271
## 45.85714 2.122382 3.035497
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 0.1590949 0.1815769 0.2023341 0.2214987 0.2391929 0.2555296 0.2706128
##  [8] 0.2845388 0.2973964 0.3092675 0.3202278 0.3303471 0.3396901 0.3483163
## 
## $高知県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -1.549679 -2.454249
## 44.14286 -1.549035 -2.465165
## 44.28571 -1.546677 -2.472548
## 44.42857 -1.543046 -2.477140
## 44.57143 -1.538486 -2.479532
## 44.71429 -1.533269 -2.480202
## 44.85714 -1.527611 -2.479533
## 45.00000 -1.521680 -2.477834
## 45.14286 -1.515610 -2.475357
## 45.28571 -1.509505 -2.472305
## 45.42857 -1.503445 -2.468840
## 45.57143 -1.497493 -2.465093
## 45.71429 -1.491695 -2.461172
## 45.85714 -1.486085 -2.457159
## 
## $高知県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 1.867869 2.772439
## 44.14286 1.912188 2.828319
## 44.28571 1.951346 2.877216
## 44.42857 1.986044 2.920137
## 44.57143 2.016872 2.957918
## 44.71429 2.044328 2.991261
## 44.85714 2.068836 3.020758
## 45.00000 2.090758 3.046912
## 45.14286 2.110403 3.070150
## 45.28571 2.128040 3.090840
## 45.42857 2.143901 3.109295
## 45.57143 2.158187 3.125788
## 45.71429 2.171075 3.140552
## 45.85714 2.182718 3.153791
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 19.14123 17.01810 17.64217 14.44429 13.38946 14.83289 19.68374 20.15920
##  [9] 18.97671 18.48547 16.13544 15.63999 16.73225 20.24448
## 
## $福岡県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                  80%        95%
## 44.00000   5.5139089  -1.699957
## 44.14286   1.0120449  -7.461047
## 44.28571   0.3462779  -8.809616
## 44.42857  -3.5578136 -13.087552
## 44.57143  -5.2294536 -15.085710
## 44.71429  -4.6851595 -15.017389
## 44.85714  -1.1902168 -12.240222
## 45.00000  -3.3268496 -15.759612
## 45.14286  -6.3553911 -19.765396
## 45.28571  -8.0768693 -22.138124
## 45.42857 -11.2299311 -25.716285
## 45.57143 -12.4236719 -27.279677
## 45.71429 -12.2022390 -27.519235
## 45.85714  -9.8734249 -25.816882
## 
## $福岡県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 32.76854 39.98241
## 44.14286 33.02415 41.49724
## 44.28571 34.93807 44.09396
## 44.42857 32.44640 41.97613
## 44.57143 32.00837 41.86463
## 44.71429 34.35093 44.68316
## 44.85714 40.55770 51.60770
## 45.00000 43.64525 56.07801
## 45.14286 44.30881 57.71882
## 45.28571 45.04782 59.10907
## 45.42857 43.50082 57.98717
## 45.57143 43.70365 58.55966
## 45.71429 45.66675 60.98374
## 45.85714 50.36239 66.30585
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 1.733274 1.733274 1.733274 1.733274 1.733274 1.733274 1.733274 1.733274
##  [9] 1.733274 1.733274 1.733274 1.733274 1.733274 1.733274
## 
## $佐賀県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%       95%
## 44.00000 -0.2715800 -1.332886
## 44.14286 -0.3471355 -1.448438
## 44.28571 -0.4200416 -1.559938
## 44.42857 -0.4905588 -1.667785
## 44.57143 -0.5589077 -1.772316
## 44.71429 -0.6252767 -1.873818
## 44.85714 -0.6898285 -1.972542
## 45.00000 -0.7527047 -2.068703
## 45.14286 -0.8140293 -2.162491
## 45.28571 -0.8739119 -2.254073
## 45.42857 -0.9324497 -2.343599
## 45.57143 -0.9897294 -2.431201
## 45.71429 -1.0458287 -2.516997
## 45.85714 -1.1008177 -2.601096
## 
## $佐賀県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 3.738129 4.799435
## 44.14286 3.813685 4.914987
## 44.28571 3.886591 5.026487
## 44.42857 3.957108 5.134334
## 44.57143 4.025457 5.238865
## 44.71429 4.091826 5.340367
## 44.85714 4.156377 5.439091
## 45.00000 4.219254 5.535252
## 45.14286 4.280578 5.629039
## 45.28571 4.340461 5.720622
## 45.42857 4.398999 5.810148
## 45.57143 4.456278 5.897749
## 45.71429 4.512378 5.983546
## 45.85714 4.567367 6.067645
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] -7.289599e-05  1.939218e-01  5.392435e-01  4.541927e-02 -3.221015e-05
##  [6]  9.547557e-06 -2.901422e-05 -4.126202e-05  1.246398e-01  3.666248e-01
## [11] -3.002641e-02 -4.732031e-05 -7.238361e-05 -4.934406e-05
## 
## $長崎県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -2.146115 -3.282161
## 44.14286 -2.194160 -3.458335
## 44.28571 -2.340774 -3.865363
## 44.42857 -3.123541 -4.801086
## 44.57143 -3.493068 -5.342169
## 44.71429 -3.760895 -5.751797
## 44.85714 -4.024527 -6.154968
## 45.00000 -4.416723 -6.754773
## 45.14286 -4.580262 -7.070886
## 45.28571 -4.643777 -7.296124
## 45.42857 -5.311699 -8.107648
## 45.57143 -5.547792 -8.484590
## 45.71429 -5.797736 -8.866833
## 45.85714 -6.039239 -9.236193
## 
## $長崎県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 2.145969 3.282015
## 44.14286 2.582004 3.846178
## 44.28571 3.419261 4.943850
## 44.42857 3.214379 4.891925
## 44.57143 3.493003 5.342105
## 44.71429 3.760914 5.751816
## 44.85714 4.024469 6.154910
## 45.00000 4.416641 6.754691
## 45.14286 4.829541 7.320166
## 45.28571 5.377027 8.029373
## 45.42857 5.251646 8.047595
## 45.57143 5.547697 8.484495
## 45.71429 5.797591 8.866689
## 45.85714 6.039141 9.236095
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 6.337229 4.898309 5.051123 5.051123 5.051123 5.051123 5.051123 5.051123
##  [9] 5.051123 5.051123 5.051123 5.051123 5.051123 5.051123
## 
## $熊本県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%        95%
## 44.00000  2.1313551 -0.0951002
## 44.14286 -0.6985204 -3.6613030
## 44.28571 -0.6994594 -3.7436337
## 44.42857 -1.0394593 -4.2636186
## 44.57143 -1.3614574 -4.7560722
## 44.71429 -1.6680423 -5.2249533
## 44.85714 -1.9612358 -5.6733542
## 45.00000 -2.2426532 -6.1037449
## 45.14286 -2.5136087 -6.5181356
## 45.28571 -2.7751890 -6.9181882
## 45.42857 -3.0283049 -7.3052954
## 45.57143 -3.2737283 -7.6806382
## 45.71429 -3.5121208 -8.0452280
## 45.85714 -3.7440540 -8.3999393
## 
## $熊本県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 10.54310 12.76956
## 44.14286 10.49514 13.45792
## 44.28571 10.80170 13.84588
## 44.42857 11.14170 14.36586
## 44.57143 11.46370 14.85832
## 44.71429 11.77029 15.32720
## 44.85714 12.06348 15.77560
## 45.00000 12.34490 16.20599
## 45.14286 12.61585 16.62038
## 45.28571 12.87743 17.02043
## 45.42857 13.13055 17.40754
## 45.57143 13.37597 17.78288
## 45.71429 13.61437 18.14747
## 45.85714 13.84630 18.50218
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1]  0.01471817 -0.05040640  0.06517045  0.08210421  0.07937643  0.07673927
##  [7]  0.07418973  0.07172490  0.06934195  0.06703817  0.06481094  0.06265770
## [13]  0.06057599  0.05856345
## 
## $大分県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -1.365927 -2.096796
## 44.14286 -1.646054 -2.490738
## 44.28571 -1.707260 -2.645527
## 44.42857 -1.759353 -2.734162
## 44.57143 -1.774137 -2.755328
## 44.71429 -1.787973 -2.775092
## 44.85714 -1.800928 -2.793556
## 45.00000 -1.813067 -2.810816
## 45.14286 -1.824447 -2.826959
## 45.28571 -1.835122 -2.842065
## 45.42857 -1.845140 -2.856207
## 45.57143 -1.854546 -2.869453
## 45.71429 -1.863383 -2.881865
## 45.85714 -1.871687 -2.893499
## 
## $大分県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 1.395363 2.126232
## 44.14286 1.545241 2.389925
## 44.28571 1.837600 2.775868
## 44.42857 1.923561 2.898370
## 44.57143 1.932890 2.914081
## 44.71429 1.941451 2.928570
## 44.85714 1.949308 2.941935
## 45.00000 1.956517 2.954266
## 45.14286 1.963131 2.965643
## 45.28571 1.969198 2.976141
## 45.42857 1.974762 2.985829
## 45.57143 1.979862 2.994768
## 45.71429 1.984535 3.003017
## 45.85714 1.988814 3.010626
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 0.1880141 0.1060299 0.1060299 0.1060299 0.1060299 0.1060299 0.1060299
##  [8] 0.1060299 0.1060299 0.1060299 0.1060299 0.1060299 0.1060299 0.1060299
## 
## $宮崎県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                80%       95%
## 44.00000 -2.520296 -3.953989
## 44.14286 -2.717373 -4.211992
## 44.28571 -3.044438 -4.712195
## 44.42857 -3.340605 -5.165143
## 44.57143 -3.613262 -5.582137
## 44.71429 -3.867253 -5.970582
## 44.85714 -4.105955 -6.335646
## 45.00000 -4.331837 -6.681102
## 45.14286 -4.546766 -7.009807
## 45.28571 -4.752195 -7.323984
## 45.42857 -4.949284 -7.625405
## 45.57143 -5.138971 -7.915507
## 45.71429 -5.322034 -8.195478
## 45.85714 -5.499122 -8.466310
## 
## $宮崎県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 2.896324 4.330017
## 44.14286 2.929433 4.424052
## 44.28571 3.256498 4.924255
## 44.42857 3.552665 5.377203
## 44.57143 3.825322 5.794196
## 44.71429 4.079313 6.182642
## 44.85714 4.318015 6.547706
## 45.00000 4.543897 6.893162
## 45.14286 4.758826 7.221867
## 45.28571 4.964255 7.536044
## 45.42857 5.161343 7.837465
## 45.57143 5.351031 8.127567
## 45.71429 5.534094 8.407538
## 45.85714 5.711181 8.678369
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 4.753862 4.303196 3.790312 4.022420 3.730886 3.730886 3.730886 3.730886
##  [9] 3.730886 3.730886 3.730886 3.730886 3.730886 3.730886
## 
## $鹿児島県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%       95%
## 44.00000  0.7527111 -1.365370
## 44.14286 -0.4646589 -2.988609
## 44.28571 -1.2381193 -3.900010
## 44.42857 -1.1389737 -3.871251
## 44.57143 -1.4886591 -4.251719
## 44.71429 -1.5024466 -4.272806
## 44.85714 -1.5161979 -4.293836
## 45.00000 -1.5299132 -4.314812
## 45.14286 -1.5435929 -4.335733
## 45.28571 -1.5572371 -4.356600
## 45.42857 -1.5708463 -4.377414
## 45.57143 -1.5844206 -4.398174
## 45.71429 -1.5979604 -4.418881
## 45.85714 -1.6114658 -4.439536
## 
## $鹿児島県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 8.755013 10.87309
## 44.14286 9.071050 11.59500
## 44.28571 8.818744 11.48063
## 44.42857 9.183814 11.91609
## 44.57143 8.950431 11.71349
## 44.71429 8.964219 11.73458
## 44.85714 8.977970 11.75561
## 45.00000 8.991685 11.77658
## 45.14286 9.005365 11.79751
## 45.28571 9.019009 11.81837
## 45.42857 9.032618 11.83919
## 45.57143 9.046193 11.85995
## 45.71429 9.059733 11.88065
## 45.85714 9.073238 11.90131
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##  [1] 23.17791 25.57328 24.55484 26.85837 26.49987 24.39266 25.66723 25.45058
##  [9] 25.45058 25.45058 25.45058 25.45058 25.45058 25.45058
## 
## $沖縄県$lower
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##                 80%         95%
## 44.00000 11.4805625   5.2883620
## 44.14286 12.7875280   6.0191580
## 44.28571 10.7663232   3.4671234
## 44.42857 12.1352277   4.3412683
## 44.57143 10.8979973   2.6388637
## 44.71429  7.9589673  -0.7405024
## 44.85714  8.4418438  -0.6767229
## 45.00000  6.4831349  -3.5576241
## 45.14286  5.4362539  -5.1586904
## 45.28571  4.4414743  -6.6800746
## 45.42857  3.4917140  -8.1326077
## 45.57143  2.5813633  -9.5248689
## 45.71429  1.7058891 -10.8637912
## 45.85714  0.8615657 -12.1550723
## 
## $沖縄県$upper
## Time Series:
## Start = c(44, 1) 
## End = c(45, 7) 
## Frequency = 7 
##               80%      95%
## 44.00000 34.87525 41.06745
## 44.14286 38.35904 45.12741
## 44.28571 38.34336 45.64256
## 44.42857 41.58151 49.37547
## 44.57143 42.10175 50.36088
## 44.71429 40.82635 49.52582
## 44.85714 42.89261 52.01118
## 45.00000 44.41803 54.45878
## 45.14286 45.46491 56.05985
## 45.28571 46.45969 57.58124
## 45.42857 47.40945 59.03377
## 45.57143 48.31980 60.42603
## 45.71429 49.19527 61.76495
## 45.85714 50.03959 63.05623